Dual-energy computed tomography for prediction of loco-regional recurrence after radiotherapy in larynx and hypopharynx squamous cell carcinoma

Bahig, Houda; Lapointe, Andréanne; Bedwani, Stéphane; Guise, Jacques de; Lambert, Louise; Filion, Édith; Roberge, David; Létourneau-Guillon, Laurent; Blais, Danis; Ng, Sweet Ping; Nguyen-Tân, Phuc Félix

doi:10.1016/j.ejrad.2018.11.005

Cited by 39 publications

(25 citation statements)

References 44 publications

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“…Various studies have been aimed at predicting other, albeit possibly controversial endpoint choices such as locoregional failure or development of distant metastases. Table 4 compares the radiomic studies that address local control (LC), locoregional control (LRC), local failure (LF), and distant metastases (DM) [39,[69][70][71][72][73][74][75][76][77].…”

Section: Risk Stratification and Prognostic/predictive Biomarkersmentioning

confidence: 99%

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

Bruixola¹,

Remacha²,

Jiménez-Pastor³

et al. 2021

Cancer Treatment Reviews

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Risk Stratification and Prognostic/predictive Biomarkersmentioning

confidence: 99%

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

Bruixola¹,

Remacha²,

Jiménez-Pastor³

et al. 2021

Cancer Treatment Reviews

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“…Besides tumor diagnosis, radiomics is also subject of investigation in predictive (90)(91)(92) and prognostic models (67,(93)(94)(95)(96), to evaluate local tumor control (97)(98)(99), and HPV status (100,101).…”

Section: Radiomicsmentioning

confidence: 99%

A Literature Review of the Potential Diagnostic Biomarkers of Head and Neck Neoplasms

et al. 2020

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Head and neck neoplasms have a poor prognosis because of their late diagnosis. Finding a biomarker to detect these tumors in an early phase could improve the prognosis and survival rate. This literature review provides an overview of biomarkers, covering the different-omics fields to diagnose head and neck neoplasms in the early phase. To date, not a single biomarker, nor a panel of biomarkers for the detection of head and neck tumors has been detected with clinical applicability. Limitations for the clinical implementation of the investigated biomarkers are mainly the heterogeneity of the study groups (e.g., small population in which the biomarker was tested, and/or only including high-risk populations) and a low sensitivity and/or specificity of the biomarkers under study. Further research on biomarkers to diagnose head and neck neoplasms in an early stage, is therefore needed.

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“…In addition to the traditional role of imaging for staging and post-treatment follow-up of HNSCC, there is increasing interest in the use of quantitative features extracted from the images or radiomic features for the characterization of HNSCC [8]. Radiomic features have demonstrated varying predictive values in recent years for molecular subgroup, HPV status, stage, locoregional control (LRC), progression-free survival (PFS), and overall survival (OS) of HNSCC [3,[9][10][11][12][13][14][15][16][17][18][19]. Using machine learning algorithms, the quantitative image-based features extracted from cervical lymph nodes have also demonstrated the potential to predict the presence of metastasis in the lymph nodes [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…So far, most of the radiomic studies evaluating HNSCC are either heavily or exclusively based on analysis of tumors arising in the oropharynx or they combine tumors from different anatomical locations, with a few exceptions [10,11,17,19]. Given that the HNSCC genetic landscape, molecular pathogenesis, risk factors, and treatment response vary significantly across different primary tumors, we hypothesize that the quantitative radiomic features of HNSCC are site-dependent, and they might affect the performance of machine learning models in endpoint prediction.…”

Section: Introductionmentioning

confidence: 99%

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models

Liu

Maleki

Muthukrishnan

et al. 2021

Cancers

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Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

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Dual-energy computed tomography for prediction of loco-regional recurrence after radiotherapy in larynx and hypopharynx squamous cell carcinoma

Cited by 39 publications

References 44 publications

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

A Literature Review of the Potential Diagnostic Biomarkers of Head and Neck Neoplasms

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models

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